Enhance player median calculations and projections in init_baselines and sim_contest functions. Added 'cpt_Median' field to raw_display DataFrame for both DraftKings and FanDuel, and updated projection logic to utilize 'cpt_projection_map' for improved accuracy in simulations.

app.py

@@ -132,6 +132,7 @@ def init_baselines(sport):
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
                                    'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+        raw_display['cpt_Median'] = raw_display['Median'] * 1.5
         raw_display['STDev'] = raw_display['Median'] / 4
         
         dk_raw = raw_display.dropna(subset=['Median'])
@@ -142,6 +143,7 @@ def init_baselines(sport):
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
                                    'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+        raw_display['cpt_Median'] = raw_display['Median']
         raw_display['STDev'] = raw_display['Median'] / 4
         
         fd_raw = raw_display.dropna(subset=['Median'])
@@ -154,6 +156,7 @@ def init_baselines(sport):
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
                                    'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp']]
+        raw_display['cpt_Median'] = raw_display['Median'] * 1.5
         raw_display = raw_display[raw_display['site'] == 'Draftkings']
         raw_display['STDev'] = raw_display['Median'] / 4
         
@@ -165,6 +168,7 @@ def init_baselines(sport):
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
                                    'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp']]
+        raw_display['cpt_Median'] = raw_display['Median']
         raw_display = raw_display[raw_display['site'] == 'Fanduel']
         raw_display['STDev'] = raw_display['Median'] / 4
         
@@ -207,9 +211,11 @@ def sim_contest(Sim_size, seed_frame, maps_dict, sharp_split, Contest_Size):
         fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)]
             
         sample_arrays1 = np.c_[
-            fp_random, 
+            fp_random,
             np.sum(np.random.normal(
-                loc=vec_projection_map(fp_random[:, :-7]),
+                loc=np.where(np.arange(fp_random.shape[1]) == 0,  # Check if it's the first column
+                    cpt_projection_map(fp_random[:, :-7]),         # Use cpt_projection_map for first column
+                    vec_projection_map(fp_random[:, :-7])),        # Use original for other columns
                 scale=vec_stdev_map(fp_random[:, :-7])),
             axis=1)
         ]
@@ -403,6 +409,7 @@ with tab1:
             if 'working_seed' in st.session_state:
                 maps_dict = {
                         'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
+                        'cpt_projection_map':dict(zip(raw_baselines.Player,raw_baselines.cpt_Median)),
                         'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
                         'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
                         'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
@@ -441,6 +448,7 @@ with tab1:
                     st.session_state.working_seed = FD_seed.copy()
                 maps_dict = {
                         'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
+                        'cpt_projection_map':dict(zip(raw_baselines.Player,raw_baselines.cpt_Median)),
                         'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
                         'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
                         'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),